Techniques for Efficient Automated Elimination of False Positives

    Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

    Samenvatting

    Static analysis tools are useful to detect common programming errors. However, they generate a large number of false positives. Postprocessing of these alarms using a model checker has been proposed to automatically eliminate false positives from them. To scale up the automated false positives elimination (AFPE), several techniques, e.g., program slicing, are used. However, these techniques increase the time taken by AFPE, and the increased time is a major concern during application of AFPE to alarms generated on large systems.

    To reduce the time taken by AFPE, we propose two techniques. The techniques achieve the reduction by identifying and skipping redundant calls to the slicer and model checker. The first technique is based on our observation that, (a) combination
    of application-level slicing, verification with incremental context, and the context-level slicing helps to eliminate more false positives; (b) however, doing so can result in redundant calls to the slicer. In this technique, we use data dependencies to compute
    these redundant calls. The second technique is based on our observation that (a) code partitioning is commonly used by static analysis tools to analyze very large systems, and (b) applying AFPE to alarms generated on partitioned-code can result in
    repeated calls to both the slicer and model checker. We use memoization to identify the repeated calls and skip them.

    The first technique is currently under evaluation. Our initial evaluation of the second technique indicates that it reduces AFPE time by up to 56%, with median reduction of 12.15%.
    Originele taal-2Engels
    TitelIEEE International Working Conference on Source Code Analysis and Manipulation
    SubtitelNew Ideas and Emerging Results
    UitgeverijIEEE Computer Society
    Pagina's248-252
    StatusGeaccepteerd/In druk - 16 sep 2020

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